需要使用spark抽样检测hive中某张表的数据是否满足数据标准数据标准包括数据类型、数据长度、数据精度、数据空值、缺省值请帮我写出具体代码

以下是使用Spark抽样检测Hive表数据是否满足数据标准的代码：

import org.apache.spark.sql.SparkSession

object DataStandardCheck {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .appName("DataStandardCheck")
      .enableHiveSupport()
      .getOrCreate()

    // 获取表名和抽样比例
    val tableName = args(0)
    val sampleRate = args(1).toDouble

    // 读取Hive表数据
    val df = spark.sql(s"SELECT * FROM $tableName")

    // 检查数据类型和数据长度
    val schema = df.schema
    schema.foreach(field => {
      val fieldName = field.name
      val fieldType = field.dataType.typeName
      val fieldLength = field.dataType.defaultSize
      val fieldMaxLength = field.metadata.getLong("maxlength")
      val fieldMinLength = field.metadata.getLong("minlength")
      val sampleData = df.select(fieldName).sample(false, sampleRate).collect()
      sampleData.foreach(row => {
        val fieldValue = row(0)
        if (fieldValue != null) {
          // 判断数据类型是否正确
          if (fieldType.toLowerCase.startsWith("decimal")) {
            assert(fieldValue.isInstanceOf[java.math.BigDecimal],
              s"Field $fieldName is not decimal type")
          } else {
            assert(fieldValue.getClass.getSimpleName.toLowerCase.startsWith(fieldType),
              s"Field $fieldName is not $fieldType type")
          }
          // 判断数据长度是否正确
          assert(fieldValue.toString.length <= fieldMaxLength,
            s"Field $fieldName length is greater than $fieldMaxLength")
          assert(fieldValue.toString.length >= fieldMinLength,
            s"Field $fieldName length is less than $fieldMinLength")
        }
      })
    })

    // 检查数据精度
    df.schema.filter(_.dataType.typeName.toLowerCase.startsWith("decimal")).foreach(field => {
      val fieldName = field.name
      val fieldPrecision = field.dataType.asInstanceOf[org.apache.spark.sql.types.DecimalType].precision
      val fieldScale = field.dataType.asInstanceOf[org.apache.spark.sql.types.DecimalType].scale
      val sampleData = df.select(fieldName).sample(false, sampleRate).collect()
      sampleData.foreach(row => {
        val fieldValue = row(0)
        if (fieldValue != null) {
          val decimalValue = fieldValue.asInstanceOf[java.math.BigDecimal]
          assert(decimalValue.precision() <= fieldPrecision,
            s"Field $fieldName precision is greater than $fieldPrecision")
          assert(decimalValue.scale() <= fieldScale,
            s"Field $fieldName scale is greater than $fieldScale")
        }
      })
    })

    // 检查数据空值和缺省值
    df.schema.foreach(field => {
      val fieldName = field.name
      val sampleData = df.select(fieldName).sample(false, sampleRate).collect()
      val nullCount = sampleData.filter(row => row(0) == null).length
      val defaultValue = field.metadata.getString("default")
      val defaultCount = sampleData.filter(row => row(0) != null && row(0).toString == defaultValue).length
      assert(nullCount == 0, s"Field $fieldName contains null value")
      assert(defaultCount == 0, s"Field $fieldName contains default value $defaultValue")
    })

    // 输出结果
    println("Data standard check passed!")
  }
}

在使用时，需要传入Hive表名和抽样比例两个参数，例如：

spark-submit --class DataStandardCheck --master yarn --deploy-mode client --executor-memory 2G --num-executors 10 data_standard_check.jar my_table 0.1

其中，my_table是要检查的Hive表名，0.1是抽样比例（即从表中随机抽取10%的数据进行检查）。如果检查通过，程序会输出Data standard check passed!。如果检查不通过，程序会抛出异常并输出错误信息